Standard

Model-based mutant equivalence detection using automata language equivalence and simulations. / Devroey, Xavier; Perrouin, Gilles; Papadakis, Mike; Legay, Axel; Schobbens, Pierre-Yves; Heymans, Patrick.

In: Journal of Systems and Software, Vol. 141, 2018, p. 1-15.

Research output: Scientific - peer-reviewArticle

Harvard

Devroey, X, Perrouin, G, Papadakis, M, Legay, A, Schobbens, P-Y & Heymans, P 2018, 'Model-based mutant equivalence detection using automata language equivalence and simulations' Journal of Systems and Software, vol 141, pp. 1-15. DOI: 10.1016/j.jss.2018.03.010

APA

Devroey, X., Perrouin, G., Papadakis, M., Legay, A., Schobbens, P-Y., & Heymans, P. (2018). Model-based mutant equivalence detection using automata language equivalence and simulations. Journal of Systems and Software, 141, 1-15. DOI: 10.1016/j.jss.2018.03.010

Vancouver

Devroey X, Perrouin G, Papadakis M, Legay A, Schobbens P-Y, Heymans P. Model-based mutant equivalence detection using automata language equivalence and simulations. Journal of Systems and Software. 2018;141:1-15. Available from, DOI: 10.1016/j.jss.2018.03.010

Author

Devroey, Xavier ; Perrouin, Gilles ; Papadakis, Mike ; Legay, Axel ; Schobbens, Pierre-Yves ; Heymans, Patrick. / Model-based mutant equivalence detection using automata language equivalence and simulations. In: Journal of Systems and Software. 2018 ; Vol. 141. pp. 1-15

BibTeX

@article{a4008f16786742e88dea6dd2f1b6e9ae,
title = "Model-based mutant equivalence detection using automata language equivalence and simulations",
abstract = "Mutation analysis is a popular technique for assessing the strength of test suites. It relies on the mutation score, which indicates their fault-revealing potential. Yet, there are mutants whose behaviour is equivalent to the original system, wasting analysis resources and preventing the satisfaction of a 100% mutation score. For finite behavioural models, the Equivalent Mutant Problem (EMP) can be transformed to the language equivalence problem of non-deterministic finite automata for which many solutions exist. However, these solutions are quite expensive, making computation unbearable when used for tackling the EMP. In this paper, we report on our assessment of a state-of-the-art exact language equivalence tool and two heuristics we proposed. We used 12 models, composed of (up to) 15,000 states, and 4710 mutants. We introduce a random and a mutation-biased simulation heuristics, used as baselines for comparison. Our results show that the exact approach is often more than ten times faster in the weak mutation scenario. For strong mutation, our biased simulations can be up to 1000 times faster for models larger than 300 states, while limiting the error of misclassifying non-equivalent mutants as equivalent to 8% on average. We therefore conclude that the approaches can be combined for improved efficiency.",
keywords = "Model-based mutation analysis, Automata language equivalence, Random simulations",
author = "Xavier Devroey and Gilles Perrouin and Mike Papadakis and Axel Legay and Pierre-Yves Schobbens and Patrick Heymans",
year = "2018",
doi = "10.1016/j.jss.2018.03.010",
volume = "141",
pages = "1--15",
journal = "Journal of Systems and Software",
issn = "0164-1212",
publisher = "Elsevier Bedrijfsinformatie",

}

RIS

TY - JOUR

T1 - Model-based mutant equivalence detection using automata language equivalence and simulations

AU - Devroey,Xavier

AU - Perrouin,Gilles

AU - Papadakis,Mike

AU - Legay,Axel

AU - Schobbens,Pierre-Yves

AU - Heymans,Patrick

PY - 2018

Y1 - 2018

N2 - Mutation analysis is a popular technique for assessing the strength of test suites. It relies on the mutation score, which indicates their fault-revealing potential. Yet, there are mutants whose behaviour is equivalent to the original system, wasting analysis resources and preventing the satisfaction of a 100% mutation score. For finite behavioural models, the Equivalent Mutant Problem (EMP) can be transformed to the language equivalence problem of non-deterministic finite automata for which many solutions exist. However, these solutions are quite expensive, making computation unbearable when used for tackling the EMP. In this paper, we report on our assessment of a state-of-the-art exact language equivalence tool and two heuristics we proposed. We used 12 models, composed of (up to) 15,000 states, and 4710 mutants. We introduce a random and a mutation-biased simulation heuristics, used as baselines for comparison. Our results show that the exact approach is often more than ten times faster in the weak mutation scenario. For strong mutation, our biased simulations can be up to 1000 times faster for models larger than 300 states, while limiting the error of misclassifying non-equivalent mutants as equivalent to 8% on average. We therefore conclude that the approaches can be combined for improved efficiency.

AB - Mutation analysis is a popular technique for assessing the strength of test suites. It relies on the mutation score, which indicates their fault-revealing potential. Yet, there are mutants whose behaviour is equivalent to the original system, wasting analysis resources and preventing the satisfaction of a 100% mutation score. For finite behavioural models, the Equivalent Mutant Problem (EMP) can be transformed to the language equivalence problem of non-deterministic finite automata for which many solutions exist. However, these solutions are quite expensive, making computation unbearable when used for tackling the EMP. In this paper, we report on our assessment of a state-of-the-art exact language equivalence tool and two heuristics we proposed. We used 12 models, composed of (up to) 15,000 states, and 4710 mutants. We introduce a random and a mutation-biased simulation heuristics, used as baselines for comparison. Our results show that the exact approach is often more than ten times faster in the weak mutation scenario. For strong mutation, our biased simulations can be up to 1000 times faster for models larger than 300 states, while limiting the error of misclassifying non-equivalent mutants as equivalent to 8% on average. We therefore conclude that the approaches can be combined for improved efficiency.

KW - Model-based mutation analysis

KW - Automata language equivalence

KW - Random simulations

U2 - 10.1016/j.jss.2018.03.010

DO - 10.1016/j.jss.2018.03.010

M3 - Article

VL - 141

SP - 1

EP - 15

JO - Journal of Systems and Software

T2 - Journal of Systems and Software

JF - Journal of Systems and Software

SN - 0164-1212

ER -

ID: 42059208